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To appear in Proceedings of IEEE Visualization ’00 (October 2000, Salt Lake City, UT). Volume Illustration: Non-Photorealistic Rendering of Volume Models David Ebert Penny Rheingans Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 [ebert | rheingan]@cs.umbc.edu Abstract and familiar views of a volume data set, at least for data that has Accurately and automatically conveying the structure of a volume an appropriate physical meaning. The second approach is only model is a problem not fully solved by existing volume rendering loosely based on the physical behavior of light through a volume, approaches. Physics-based volume rendering approaches create using instead an arbitrary transfer function specifying the images which may match the appearance of translucent materials appearance of a volume sample based on its value and an in nature, but may not embody important structural details. accumulation process that is not necessarily based on any actual Transfer function approaches allow flexible design of the volume accumulation mechanism [Levoy90]. This approach allows the appearance, but generally require substantial hand tuning for each designer to create a wider range of appearances for the volume in new data set in order to be effective. We introduce the volume the visualization, but sacrifices the familiarity and ease of illustration approach, combining the familiarity of a physics- interpretation of the more physics-based approach. based illumination model with the ability to enhance important We propose a new approach to volume rendering: the features using non-photorealistic rendering techniques. Since augmentation of a physics-based rendering process with non- features to be enhanced are defined on the basis of local volume photorealistic rendering (NPR) techniques [Winkenbach94, characteristics rather than volume sample value, the application Salisbury94] to enhance the expressiveness of the visualization. of volume illustration techniques requires less manual tuning than NPR draws inspiration from such fields as art and technical the design of a good transfer function. Volume illustration illustration to develop automatic methods to synthesize images provides a flexible unified framework for enhancing structural with an illustrated look from geometric surface models. Non- perception of volume models through the amplification of photorealistic rendering research has effectively addressed both features and the addition of illumination effects. the illustration of surface shape and the visualization of 2D data, but has virtually ignored the rendering of volume models. We CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional describe a set of NPR techniques specifically for the visualization Graphics and Realism – color, shading, and texture; I.3.8 of volume data, including both the adaptation of existing NPR [Computer Graphics]: Applications. techniques to volume rendering and the development of new Keywords: Volume rendering, non-photorealistic rendering, techniques specifically suited for volume models. We call this illustration, lighting models, shading, visualization. approach volume illustration. The volume illustration approach combines the benefits of the two traditional volume rendering approaches in a flexible and 1 Introduction parameterized manner. It provides the ease of interpretation resulting from familiar physics-based illumination and For volume models, the key advantage of direct volume rendering accumulation processes with the flexibility of the transfer over surface rendering approaches is the potential to show the function approach. In addition, volume illustration provides structure of the value distribution throughout the volume, rather flexibility beyond that of the traditional transfer function, than just at selected boundary surfaces of variable value (by including the capabilities of local and global distribution analysis, isosurface) or coordinate value (by cutting plane). The and light and view direction specific effects. Therefore, volume contribution of each volume sample to the final image is illustration techniques can be used to create visualizations of explicitly computed and included. The key challenge of direct volume data that are more effective at conveying the structure volume rendering is to convey that value distribution clearly and within the volume than either of the traditional approaches. As accurately. In particular, showing each volume sample with full the name suggests, volume illustration is intended primarily for opacity and clarity is impossible if volume samples in the rear of illustration or presentation situations, such as figures in the volume are not to be completely obscured. textbooks, scientific articles, and educational video. Traditionally, volume rendering has employed one of two approaches. The first attempts a physically accurate simulation of a process such as the illumination and attenuation of light in a 2 Related Work gaseous volume or the attenuation of X-rays through tissue Traditional volume rendering spans a spectrum from the accurate [Kajiya84, Drebin88]. This approach produces the most realistic to the ad hoc. Kajiya's original work on volume ray tracing for generating images of clouds [Kajiya84] incorporated a physics- based illumination and atmospheric attenuation model. This work in realistic volume rendering techniques has been extended by numerous researchers [Nishita87, Ebert90, Krueger91, Williams92, Max95, Nishita98]. In contrast, traditional volume rendering has relied on the use of transfer functions to produce artificial views of the data to highlight regions of interest [Drebin88]. These transfer functions, however, require in-depth knowledge of the data and need to be adjusted for each data set. To appear in Proceedings of IEEE Visualization ’00 (October 2000, Salt Lake City, UT). The design of effective transfer functions is still an active • Volume sample location and value research area [Fang98, Kindlmann98, Fujishiro99]. While transfer functions can be effective at bringing out the structure in • Local volumetric properties, such as gradient and minimal the value distribution of a volume, they are limited by their change direction dependence on voxel value as the sole transfer function domain. • View direction In contrast, there has been extensive research for illustrating surface shape using non-photorealistic rendering techniques. • Light information Adopting a technique found in painting, Gooch et al. developed a The view direction and light information allows global tone-based illumination model that determined hue, as well as orientation information to be used in enhancing local volumetric intensity, from the orientation of a surface element to a light features. Combining this rendering information with user selected source [Gooch98]. The extraction and rendering of silhouettes parameters provides a powerful framework for volumetric and other expressive lines has been addressed by several enhancement and modification for artistic effects. researchers [Saito90, Salisbury94, Gooch99, Interrante95]. Volumetric illustration differs from surface-based NPR in Expressive textures have been applied to surfaces to convey several important ways. In NPR, the surfaces (features) are well surface shape [Rheingans96, Salisbury97, Interrante97]. defined, whereas with volumes, feature areas within the volume A few researchers have applied NPR techniques to the must be determined through analysis of local volumetric display of data. Laidlaw used concepts from painting to create properties. The volumetric features vary continuously throughout visualizations of 2D data, using brushstroke-like elements to three-dimensional space and are not as well defined as surface convey information [Laidlaw98] and a painterly process to features. Once these volumetric feature volumes are identified, compose complex visualizations [Kirby99]. Treavett has user selected parametric properties can be used to enhance and developed techniques for pen-and-ink illustrations of surfaces illustrate them. within volumes [Treavett00]. Interrante applied principles from We begin with a volume renderer that implements physics- technical illustration to convey depth relationships with halos based illumination of gaseous phenomena. The opacity transfer around foreground features in flow data [Interrante98]. Saito function that we are using is the following simple power function: converted 3D scalar fields into a sampled point representation and ov = (k os vi ) oe visualized selected points with a simple primitive, creating an k NPR look [Saito94]. With the exceptions of the work of Saito and Interrante, the use of NPR techniques has been confined to where vi is the volume sample value and kos is the scalar surface rendering. controlling maximum opacity. Exponent koe values less than 1 soften volume differences and values greater than 1 increase the 3 Approach contrast within the volume. Figure 1 shows gaseous illumination of an abdominal CT We have developed a collection of volume illustration techniques volume of 256×256×128 voxels. In this image, as in others of that adapt and extend NPR techniques to volume objects. Most this dataset, the scene is illuminated by a single light above the traditional volume enhancement has relied on functions of the volume and slightly toward the viewer. The structure of tissues volume sample values (e.g., opacity transfer functions), although and organs is difficult to understand. In Figure 2, a transfer some techniques have also used the volume gradient (e.g., function has been used to assign voxel colors which mimic those [Levoy90]). In contrast, our volume illustration techniques are found in actual tissue. The volume is illuminated as before. fully incorporated into the volume rendering process, utilizing Organization of tissues into organs is clear, but the interiors of viewing information, lighting information, and additional structures are still unclear. We chose to base our examples on an volumetric properties to provide a powerful, easily extensible atmospheric illumination model, but the same approach can be framework for volumetric enhancement. Comparing Diagram 1, easily applied to a base renderer using Phong illumination and the traditional volume rendering system, and Diagram 2, our linear accumulation. volume illustration rendering system, demonstrates the difference In the following two sections, we describe our current in our approach to volume enhancement. By incorporating the collection of volume illustration techniques. These techniques enhancement of the volume sample's color, illumination, and can be applied in almost arbitrary amounts and combinations, opacity into the rendering system, we can implement a wide becoming a flexible toolkit for the production of expressive range of enhancement techniques. The properties that can be images of volume models. The volume illustration techniques we incorporated into the volume illustration procedures include the following: Volume Illustration Rendering Pipeline Traditional Volume Rendering Pipeline volume values f1(xi) Volume values f1(xi) shading classification Volume Transfer function Rendering voxel colors cλ(xi) voxel opacities α(xi) Volume Illustration Volume Illustration shaded, segmented volume [cλ(xi), α(xi)] color modification opacity modification resampling and compositing (raycasting, splatting, etc.) Final volume sample [cλ(xi), α(xi)] image pixels Cλ(ui) image pixels Cλ(ui) Diagram 1. Traditional Volume Rendering Pipeline. Diagram 2. Volume Illustration Rendering Pipeline. To appear in Proceedings of IEEE Visualization ’00 (October 2000, Salt Lake City, UT). propose are of two basic types: feature enhancement, and depth Figure 3 shows the effect of boundary enhancement in the and orientation cues. medical volume. The edges of the lungs and pulmonary vasculature can be seen much more clearly than before, as well as 4 Feature Enhancement some of the internal structure of the kidney. Parameter values used in Figure 3 are kgc = 0.7, kgs = 10, kge = 2.0. In a surface model, the essential feature is the surface itself. The surface is explicitly and discretely defined by a surface model, 4.2 Oriented Feature Enhancement: making “surfaceness” a boolean quality. Many other features, such as silhouettes or regions of high curvature, are simply Silhouettes, Fading, and Sketch Lines interesting parts of the surface. Such features can be identified Surface orientation is an important visual cue that has been by analysis of regions of the surface. successfully conveyed by artists for centuries through numerous In a volume model, there are no such discretely defined techniques, including silhouette lines and orientation-determined features. Volume characteristics and the features that they saturation effects. Silhouette lines are particularly important in indicate exist continuously throughout the volume. However, the the perception of surface shape, and have been utilized in surface boundaries between regions are still one feature of interest. The illustration and surface visualization rendering [Salisbury94, local gradient magnitude at a volume sample can be used to Interrante95]. Similarly, silhouette volumes increase the indicate the degree to which the sample is a boundary between perception of volumetric features. disparate regions. The direction of the gradient is analogous to In order to strengthen the cues provided by silhouette the surface normal. Regions of high gradient are similar to volumes, we increase the opacity of volume samples where the surfaces, but now “surfaceness” is a continuous, volumetric gradient nears perpendicular to the view direction, indicated by a quality, rather than a boolean quality. We have developed several dot product between gradient and view direction which nears volume illustration techniques for the enhancement of volume zero. The silhouette enhancement is described by: features based on volume gradient information. 4.1 Boundary Enhancement ( o s = ov k sc + k ss (1 − abs (∇ fn ⋅ V )) se k ) where ksc controls the scaling of non-silhouette regions, kss Levoy [Levoy90] introduced gradient-based shading and opacity controls the amount of silhouette enhancement, and kse controls enhancement to volume rendering. In his approach, the opacity the sharpness of the silhouette curve. of each voxel was scaled by the voxel's gradient magnitude to Figure 4 shows the result of both boundary and silhouette emphasize the boundaries between data (e.g., tissue) of different enhancement in the medical volume. The fine honeycomb densities and make areas of constant density transparent (e.g., structure of the liver interior is clearly apparent, as well as organ interiors). We have adapted this idea to allow the user to additional internal structure of the kidneys. Parameter values selectively enhance the density of each volume sample by a used in Figure 4 are kgc = 0.8, kgs = 5.0, kge = 1.0; ksc = 0.9, kss = function of the gradient. Assume a volume data set containing a 50, kse = 0.25. precomputed set of sample points. The value at a location Pi is a Decreasing the opacity of volume features oriented toward scalar given by: the viewer emphasizes feature orientation, and in the extreme ( ) cases, can create sketches of the volume, as illustrated in Figure vi = f Pi = f ( xi , y i , z i ) 5. Figure 5 shows a black and white sketch of the medical dataset by using a white sketch color and making non volumetric silhouettes transparent. To get appropriate shadowing of the We can also calculate the value gradient ∇f (Pi) at that location. sketch lines, the shadows are calculated based on the original In many operations we will want that gradient to be normalized. volume opacity. Using a black silhouette color can also be We use ∇fn to indicate the normalized value gradient. effective for outlining volume data. Before enhancement, voxel values are optionally mapped Orientation information can also be used effectively to through a standard transfer function which yields color value cv change feature color. For instance, in medical illustration the and opacity ov for the voxel. If no transfer function is used, these portions of anatomical structures oriented toward the viewer are values can be set to constants for the whole volume. The desaturated and structures oriented away from the view are inclusion of a transfer function allows artistic enhancements to darkened and saturated [Clark99]. We simulate these effects by supplement, rather than replace, existing volume rendering allowing the volumetric gradient orientation to the viewer to mechanisms. modify the color, saturation, value, and transparency of the given We can define a boundary-enhanced opacity for a voxel by volume sample. The use of the HSV color space allows the combining a fraction of the voxel’s original opacity with an system to easily utilize the intuitive color modification techniques enhancement based on the local boundary strength, as indicated of painters and illustrators. Figure 10 shows oriented changes in by the voxel gradient magnitude. The gradient-based opacity of the saturation and value of the medical volume. In this figure, the the volume sample becomes: color value (V) is decreased as the angle between the gradient and ( o g = o v k gc + k gs ∇ ( f ) k ge ) the viewer increases, simulating more traditional illustration techniques of oriented fading. where ov is original opacity and ∇f is the value gradient of the volume at the sample under consideration. This equation allows 5 Depth and Orientation Cues the user to select a range of effects from no gradient enhancement Few of the usual depth cues are present in traditional rendering of (kgc=1, kgs=0) to full gradient enhancement (kgs >=1) to only translucent volumes. Obscuration cues are largely missing since showing areas with large gradients (kgc=0), as in traditional there are no opaque objects to show a clear depth ordering. volume rendering. The use of the power function with exponent Perspective cues from converging lines and texture compression kge allows the user to adjust the slope of the opacity curve to best are also lacking, since few volume models contain straight lines highlight the dataset. or uniform textures. The dearth of clear depth cues makes To appear in Proceedings of IEEE Visualization ’00 (October 2000, Salt Lake City, UT). understanding spatial relationships of features in the volume relationships in 3D flow data using Line Integral Convolution difficult. One common approach to this difficulty is the use of (LIC). She created a second LIC volume with a larger element hard transfer functions, those with rapidly increasing opacity at size, using this second volume to impede the view. Special care particular value ranges of interest. While this may increase depth was required to keep objects from being obscured by their own cues by creating the appearance of surfaces within the volume, it halos. The resulting halos achieved the desired effect, but the does so by hiding all information in some regions of the volume, method depended on having flow data suitable for processing sacrificing a key advantage of volume rendering. with LIC. Similarly, information about the orientation of features We introduce a more general method for creating halo within the volume is also largely missing. Many volume effects during the illumination process using the local spatial rendering systems use very simple illumination models and often properties of the volume. Halos are created primarily in planes do not include the effect of shadows, particularly volume self- orthogonal to the view vector by making regions just outside shadowing to improve performance, even though many volume features darker and more opaque, obscuring background elements shadowing algorithms have been developed [Ebert90, Kajiya84]. which would otherwise be visible. The strongest halos are Accurate volumetric shadowing often produces subtle effects created in empty regions just outside (in the plane perpendicular which do not provide strong three-dimensional depth cues. As a to the view direction) of a strong feature. result, the shape of individual structures within even illuminated The halo effect at a voxel is computed from the distance volumes is difficult to perceive. weighted sum of haloing influences in a specified neighborhood. We have developed several techniques for the enhancement In order to restrict halos to less interesting regions, summed of depth and orientation cues in volume models, inspired by influences are weighted by the complement of the voxel’s shading concepts in art and technical illustration. gradient. The size of the halo effect is given by: neighbors h 5.1 Distance color blending hi = ∑ n P −P 2 n ( 1− ∇ (P) f i ) Intensity depth-cuing is a well known technique for enhancing the i n perception of depth in a scene [Foley96]. This technique dims where hn is the maximum potential halo contribution of a the color of objects far from the viewer, creating an effect similar neighbor. The haloing influence of a neighbor is inversely related to viewing the scene through haze. We have adapted this to its distance and the tendency of a location to be a halo is technique for volume rendering, dimming volume sample colors inversely related to its gradient magnitude. as they recede from the viewer. In addition, we have augmented The maximum potential halo contribution of each neighbor the standard intensity depth-cuing with a subtle color modulation. is proportional to the product of the alignment of the neighbor’s This color modulation increases the amount of blue in the colors gradient with the direction to the voxel under consideration of more distant volume samples, simulating techniques used for (calculated from the dot product between them) and the degree to centuries by painters, such as aerial perspective [daVinci1506, which the neighbor’s gradient is aligned perpendicular to the Beirstadt1881]. This technique exploits the tendency of cool view direction (also calculated as a dot product). The halo colors (such as blue) to recede visually while warm colors (such potential (hn) is given by: as red) advance. (P − Pn ) k hpe (1 − ∇ (P ) ⋅ V ) Depth-cued colors start as the voxel color at the front of the hn = ∇ fn (Pn ) ⋅ i k hse volume, decreasing in intensity and moving toward the background color as depth into the volume increases. The P − P fn n progression of depth-cuing need not be linear; we use an i n exponential function to control the application of depth-cuing. where khpe controls how directly the neighbor’s gradient must be The distance color blending process can be described by: oriented toward the current location, and khse controls how tightly ( )c halos are kept in the plane orthogonal to the view direction. The c d = 1 − k ds d v + k ds d v de cb k de k most strong halo effects will come from neighbors that are v displaced from the volume sample of interest in a direction where kds controls the size of the color blending effect, kde orthogonal to the view direction and that have a large gradient in controls the rate of application of color blending, dv is the fraction the direction of this sample. of distance through the volume, and cb is a defined background Once the size of the halo effect has been determined, color. When cb is a shade of grey (cb = (a, a, a) for some value of parameters control the visual appearance of halo regions. The a), only standard intensity depth-cuing is performed. Using a most common adjustment to the halo region is to decrease the background color that is a shade of blue (cb = (a, b, c) for c > a, brightness by a scalar times the halo effect and increase the b), introduces a cool shift in distant regions. Other color opacity by another scalar times the halo effect. This method modulation effects are clearly possible, but make less sense produces effects similar to those of Interrante, but can be applied perceptually. to any type of data or model during the illumination process. Figure 6 shows the effect of distance color blending. The Since the halos generated are inherently view dependent, no ribs behind the lungs fade into the distance and the region around special processing must be done to keep features from casting a the kidneys seems to recede slightly. Color blending parameters halo on themselves. used in Figure 6 are cb = (0, 0, 0.15), kds = 1.0, kse = 0.5. Figure 6 shows the effectiveness of adding halos to the medical volume. Structures in the foreground, such as the liver 5.2 Feature halos and kidneys, stand out more clearly. Halo parameters used in Figure 6 are khpe = 1.0 and khse = 2.0. Illustrators sometimes use null halos around foreground features to reinforce the perception of depth relationships within a scene. The effect is to leave the areas just outside surfaces empty, even 5.3 Tone shading if an accurate depiction would show a background object in that Another illustrative technique used by painters is to modify the place. Interrante [Interrante98] used a similar idea to show depth tone of an object based on the orientation of that object relative to To appear in Proceedings of IEEE Visualization ’00 (October 2000, Salt Lake City, UT). the light. This technique can be used to give surfaces facing the together with colors from a transfer function. The tone effects are light a warm cast while surfaces not facing the light get a cool subtler, but still improve shape perception. The basic tissue cast, giving effects suggestive of illumination by a warm light colors are preserved, but the banded structure of the aorta is more source, such as sunlight. Gooch et al. proposed an illumination apparent than in a simple illuminated and color-mapped image model based on this technique [Gooch98], defining a (Figure 2). Tone shading parameters used in Figures 7 and 8 are parameterized model for effects from pure tone shading to pure kty = 0.3, ktb = 0.3, kta = 1.0, ktd = 0.6. illuminated object color. The parameters define a warm color by combining yellow and the scaled fully illuminated object color. Similarly, a cool color combines blue and the scaled ambient 6 Application Examples We have also applied the techniques in the previous sections illuminated object color. The final surface color is formed by interpolation between the warm and cool color based on the to several other scientific data sets. Figures 10 and 11 are volume rendered images from a 256x256x64 MRI dataset of a tomato signed dot product between the surface normal and light vector. from Lawrence Berkeley National Laboratories. Figure 10 is a The model assumes a single light source, generally located above the scene. normal gas-based volume rendering of the tomato where a few of the internal structures are visible. Figure 11 has our volume We implemented an illumination model similar to Gooch illustration gradient and silhouette enhancements applied, tone shading for use with volume models. As with Gooch tone shading, the tone contribution is formed by interpolation between resulting in a much more revealing image showing the internal structures within the tomato. Parameters used in Figure 11 are the warm and cool colors based on the signed dot product between the volume sample gradient and the light vector. Unlike kgc= 0.5, kgs= 2.5, kge= 3.0; ksc= 0.4, kss= 500, kse= 0.3. Gooch tone shading, the illuminated object contribution is Figure 12 shows a 512x512x128 element flow data set from the time series simulation of unsteady flow emanating from a 2D calculated using only the positive dot product, becoming zero at rectangular slot jet. The 2D jet source is located at the left of the orientations orthogonal to the light vector. This more closely matches familiar diffuse illumination models. image and the flow is to the right. Flow researchers notice that both Figures 12 and 13 resemble Schlieren photographs that are The color at a voxel is a weighted sum of the illuminated traditionally used to analyze flow. Figure 13 shows the gaseous color (including any traditional transfer function calculations) and the total tone and directed shading from all effectiveness of boundary enhancement, silhouette enhancement, and tone shading on this data set. The overall flow structure, directed light sources. The new tone illumination model is given vortex shedding, and helical structure are much easier to perceive by: in Figure 13 than in Figure 12. NL c = k ta I G + ∑ (I t + k td I o ) Figures 14 and 15 are volume renderings of a 64x64x64 high-potential iron protein data set. Figure 14 is a traditional gas- i based rendering of the data. Figure 15 has our tone shading where kta controls the amount of gaseous illumination (IG) volume illustration techniques applied, with parameters kty = included, NL is the number of lights, ktd controls the amount of 0.15, ktb = 0.15, kta = 1.0, ktd = 0.6. The relationship of directed illumination included, It is the tone contribution to structure features and the three-dimensional location of the volume sample color, and Io is the illuminated object color features is much clearer with the tone-based shading contribution. Although this model allows for multiple light enhancements applied. sources, more than a few is likely to result in confusing images, since we are not used to interpreting complex illumination coming from many lights. 7 Conclusions The tone contribution from a single light source is interpolated from the warm and cool colors, depending on the We have introduced the concept of volume illustration, angle between the light vector and the sample gradient. It is combining the strengths of direct volume rendering with the given by: expressive power of non-photorealistic rendering techniques. I t = ((1.0 + ∇ fn ⋅ L) / 2)cw + (1 − (1.0 + ∇ fn ⋅ L) / 2)cc Volume illustration provides a powerful unified framework for producing a wide range of illustration styles using local and where L is the unit vector in the direction of the light and global properties of the volume model to control opacity cw= (kty, kty, 0), cc = (0, 0, ktb) accumulation and illumination. Volume illustration techniques describe the warm and cool tone colors. Samples oriented toward enhance the perception of structure, shape, orientation, and depth the light become more like the warm color while samples relationships in a volume model. Comparing standard volume oriented away from the light become more like the cool color. rendering (Figures 2, 10, 12, 14) with volume illustration images The directed illumination component is related to the angle (Figures 3, 4, 5, 6, 7, 8, 9, 11, 13, 15) clearly shows the power of between the voxel gradient and the light direction, for angles up employing volumetric illustration techniques to enhance 3D depth to 90 degrees. It is given by: perception and volumetric feature understanding. k td I i (∇ fn ⋅ L ) : ∇ fn ⋅ L > 0 8 Future Directions Io = We plan on extending our collection of NPR techniques and 0 : ∇ fn ⋅ L ≤ 0 exploring suitability of these volume illustration techniques for where ktd controls how much directed illumination is added. data exploration and diagnosis. Figure 7 shows modified tone shading applied to the uncolored medical volume. The small structure of the liver 9 Acknowledgements shows clearly, as does the larger structures of the kidney. The We would like to thank researchers at the Mississippi State bulges of intestine at the lower right are much more clearly University NSF Computational Field Simulation Engineering rounded 3D shapes than with just boundary and silhouette Research Center and the Armed Forces Institute of Pathology for enhancement (Figure 4). Figure 8 shows tone shading applied help in evaluating the effectiveness of these technique and To appear in Proceedings of IEEE Visualization ’00 (October 2000, Salt Lake City, UT). guiding our research. We would also like to thank Dr. Elliott Applications, 18(4), pp. 49-53 (July - August 1998). ISSN Fishman of Johns Hopkins Medical Institutions for the abdominal 0272-1716. CT dataset. The iron protein dataset came from the vtk website [Kajiya84] James T. Kajiya and Brian P. Von Herzen. Ray (www.kitware.com/vtk.html). Christopher Morris generated Tracing Volume Densities, Computer Graphics some to the pictures included in this paper. This work supported (Proceedings of SIGGRAPH 84), 18(3), pp. 165-174 (July in part by the National Science Foundation under Grants ACIR 1984, Minneapolis, Minnesota). Edited by Hank 9996043 and ACIR 9978032. Christiansen. [Kindlmann98] Gordon Kindlmann and James Durkin. 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Edited by Andrew Glassner. ISBN 0-89791- 667-0. Figure 12. Atmospheric volume rendering of square jet. No illustration enhancements. Figure 1. Gaseous illumination of medical Figure 5. Volumetric sketch lines on CT CT volume. Voxels are a constant color. volume. Lines are all white. To appear in Proceedings of IEEE Visualization ’00 (October 2000, Salt Lake City, UT). Figure 2. Gaseous illumination of color- Figure 3. Color-mapped gaseous Figure 4. Silhouette and boundary mapped CT volume. illumination with boundary enhancement. enhancement of CT volume. Figure 6. Distance color blending and Figure 7. Tone shading in CT volume. Figure 8. Tone shading in colored volume. halos around features of CT volume. Surfaces toward light receive warm color. Surfaces toward light receive warm color. Figure 10. Standard atmospheric Figure 11. Boundary and silhouette volume rendering of tomato. enhanced tomato. Figure 9. Orientation fading. Surfaces toward viewer are desaturated. Figure 13. Square jet with boundary and Figure 14. Atmospheric rendering of Figure 15. Tone shaded iron protein. silhouette enhancement, and tone shading. iron protein.
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